Improvement of Accuracy of Content-Based Image Retrieval Using Local and Statistical Methods
محورهای موضوعی : Majlesi Journal of Telecommunication DevicesNarges Savoj 1 , Bijan Shoushtarian 2
1 - ...
2 -
کلید واژه: Statistical features, Image retrieval, Local Image Features, Distance-based Identification, Content-Based Image Retrieval methods,
چکیده مقاله :
Content-based image retrieval (CBIR) system plays an important role in retrieving desired images from a large database of images. These programs in all areas, including hospitals, regulatory applications (surveillance), architecture, journalism and many other items found in the role. In initial research text-based image retrieval was performed, but with the advent of great challenges in text-based retrieval (eg spelling errors), content-based image retrieval has been introduced by researchers, which is by far the most effective method for image retrieval. Content-based image retrieval system uses features such as color, shape and texture. To extract the tissue properties local binary patterns and edge filtering methods are of particular popularity among researchers. A review of the methods presented so far shows that despite the quality of the descriptors and categories and retrieval methods, none of these methods can meet the needs and challenges of the present, so to improve the accuracy of image retrieval, in this study, a method introduced. To extract feature from the images, five color histogram descriptors, color moment, edge histogram, gradient oriented histogram and MRELBP were used. To classify the attributes extracted by the descriptors, three categories of support vector machine and k nearest neighbour and random forest are used. In the method, the features extracted by the five descriptors are combined and after classifying and identifying the test image class, using the Kmeans cluster, the closest images to the test image are retrieved from the identified class. Experimental results method on three databases Corel 4k, Wang and Corel 5k show We have accomplished the highest precision rate of 86% using proposed CBIR system.
[1] A. N. Tikle, C. Vaidya, and P. Dahiwale, "Notice of Retraction A Survey of Indexing Techniques For Large Scale Content-Based Image Retrieval," in 2015 International Conference on Electrical, Electronics, Signals, Communication and Optimization (EESCO) :5112 ,IEEE, pp. 1-5.
[2] P. Mueller, "Color image retrieval from monochrome transparencies," Applied optics, vol. 8, no. 10, pp. 2051-2057, 1969.
[3] V. N. Gudivada and V. V. Raghavan, "Content based image retrieval systems," Computer, vol. 28, no. 9, pp. 18-22, 1995.
[4] J. A. da Silva Júnior, R. E. Marçal, and M. A. Batista, "Image retrieval: importance and applications," in Workshop de Vis~ ao Computacional-WVC, 2014.
[5] M. S. Lew, N. Sebe, and J. P. Eakins, "Challenges of image and video retrieval ",in International Conference on Image and Video Retrieval, 2002: Springer, pp. 1-6.
[6] X.-y. Wang, Z.-f. Chen, and J.-j. Yun, "An effective method for color image retrieval based on texture," Computer Standards & Interfaces, vol. 34, no. 1, pp. 31-35, 2 .115
[7] G.-H. Liu and J.-Y. Yang, "Content-based image retrieval using color difference histogram," Pattern recognition, vol. 46, no. 1, pp. 188-198, 2013.
[8] A. Giri and Y. K. Meena, "Content based image retrieval using integration of color and texture features," International Journal of Advanced Research in
[9] A. Huneiti and M. Daoud, "Content-based image retrieval using SOM and DWT," Journal of Software Engineering and Applications, vol. 8, no. 02, p .5112 ,21 .
[10] M. Kaur and N. Sohi, "A novel technique for content based image retrieval using color, texture and edge features," in 2016 international conference on communication and electronics systems (ICCES), 2016: IEEE, pp. 1-7.
[11] S. Fadaei, R. Amirfattahi, and M. R. Ahmadzadeh, "New content-based image retrieval system based on optimised integration of DCD, wavelet and curvelet features," IET Image Processing, vol. 11, no. 2, pp. 89-98, 2016.
[12] N. Sai and R. C. Patil, "DCT-SVD domain feature vector for image retrieval," in 2017 International Conference on Signal Processing and Communication (ICSPC), 2017: IEEE, pp. 477-482.
[13] N. Jain and S. Salankar, "Content based image retrieval using improved gabor wavelet transform and linear discriminant analysis," in 2018 3rd International Conference for Convergence in Technology (I2CT), 2018: IEEE, pp. 1-4.
[14] X. Tian, Q. Zheng, and J. Xing, "Content-Based Image Retrieval System Via Deep Learning Method," in 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), 2018: IEEE, pp. 1257-1261.
[15] L. Liu, S. Lao, P. W. Fieguth, Y. Guo, X. Wang, and M. Pietikäinen, "Median robust extended local binary pattern for texture classification," IEEE Transactions on Image Processing ,vol. 25, no. 3, pp. 1368-1381, 2016.
